Core Edges Research Articles

A multi-objective optimization model for cropland design considering profit, biodiversity, and ecosystem services

More sustainable agricultural methods are needed to alleviate the decreases in biodiversity and ecosystem services that have occurred because of industrial agriculture. One such method is the inclusion of alternative crops into croplands that can support biodiversity, reduce erosion and chemical runoff, and sequester carbon in the soil. However, the question of where such crops should be planted to balance competing economic and environmental objectives remains open. To this end, we develop a mixed-integer quadratically constrained program to optimize the layout of a cropland considering economic, biodiversity, greenhouse gas emissions, and water quality objectives. We include spatially varying fertilization as a decision variable in addition to crop establishment location. We further include the effect of core area and edges between different crops on biodiversity. To demonstrate the applicability of the model, we apply it to an example field, showing how the optimal cropland design changes as a decision-maker prioritizes different objectives and as edges have different impacts on biodiversity.

Passive acoustic surveys demonstrate high densities of sperm whales off the mid-Atlantic coast of the USA in winter and spring

Oceans are increasingly crowded by anthropogenic activities yet the impact on Outer Continental Shelf (OCS) marine life remains largely unquantified. The MAPS (Marine Mammal Acoustic and Spatial Ecology) study of 2019 included passive acoustic and visual vessel surveys over the Mid-Atlantic OCS of the USA to address data gaps in winter/spring for deep-diving cetaceans, including sperm whales. Echolocation clicks were used to derive slant ranges to sperm whales for design- and model-based density estimates. Although more survey effort was realised in the spring, high densities of whales were identified in both winter and spring (10.46 and 8.89 per 1000 km2 respectively). The spring model-based abundance estimate of 1587 whales (CI 946–2663) was considered the most representative figure, in part due to lower coefficients of variation. Modelling suggested that high densities of whales were associated with warm core rings, eddies and edges. As OCS waters provide an important foraging habitat for North Atlantic sperm whales, appropriate mitigation is required to ensure commercial pressures to develop offshore energy do not negatively affect this endangered species.

Performance of microbial inoculation and tricalcium phosphate on nitrogen retention and conversion: Core microorganisms and enzyme activity during kitchen waste composting

The substantial release of NH3 during composting leads to nitrogen (N) losses and poses environmental hazards. Additives can mitigate nitrogen loss by adsorbing NH3/NH4, adjusting pH, and enhancing nitrification, thereby improving compost quality. Herein, we assessed the effects of combining bacterial inoculants (BI) (1.5%) with tricalcium phosphate (CA) (2.5%) on N retention, organic N conversion, bacterial biomass, functional genes, network patterns, and enzyme activity during kitchen waste (KW) composting. Results revealed that adding of 1.5%/2.5% (BI + CA) significantly (p < 0.05) improved ecological parameters, including pH (7.82), electrical conductivity (3.49 mS/cm), and N retention during composting. The bacterial network properties of CA (265 node) and BI + CA (341 node) exhibited a substantial niche overlap compared to CK (210 node). Additionally, treatments increased organic N and total N (TN) content while reducing NH4+-N by 65.42% (CA) and 77.56% (BI + CA) compared to the control (33%). The treatments, particularly BI + CA, significantly (p < 0.05) increased amino acid N, hydrolyzable unknown N (HUN), and amide N, while amino sugar N decreased due to bacterial consumption. Network analysis revealed that the combination expanded the core bacterial nodes and edges involved in organic N transformation. Key genes facilitating nitrogen mediation included nitrate reductase (nasC and nirA), nitrogenase (nifK and nifD), and hydroxylamine oxidase (hao). The structural equation model suggested that combined application (CA) and microbial inoculants enhance enzyme activity and bacterial interactions during composting, thereby improving nitrogen conversion and increasing the nutrient content of compost products.

Understanding the mechanisms of green tea EGCG against amyloid β oligomer neurotoxicity through computational studies.

Oligomeric species of amyloid β peptide (Aβ) are pivotal in Alzheimer's disease (AD) pathogenesis, making them valuable therapeutic targets. Currently, there is no cure or preventive therapy available for AD, with only a few therapeutics offering temporary alleviation of symptoms. Natural products (NPs) are now considered promising anti-amyloid agents. Green tea catechins have garnered considerable attention due to their ability to remodel the toxic amyloid β peptide oligomers (AβOs) into non-toxic assemblies. Nevertheless, the precise molecular mechanism underlying their effects on AβOs remains unclear. In this study, we employ a combination of binding site prediction, molecular docking, and dynamics simulations to gain mechanistic insights into the binding of the potent anti-amyloid epigallocatechin-3-gallate (EGCG) and the less effective catechin, epicatechin (EC), on the structure of pore-forming Aβ tetramers (PDB ID 6RHY). This recently elucidated structure represents AβO(1-42) with two faces of the hydrophobic β-sheet core and hydrophilic edges. Our simulations revealed three potential druggable binding sites within the AβO: two in hydrophilic edges and one in the β-sheet core. Although both catechins bind via hydrogen bond (H-bond) and aromatic interactions to the three potential binding sites, EGCG interacted with key residues more efficiently than EC. We propose that EGCG may remodel AβOs preventing pore formation by binding to the hydrophilic edge binding sites. Additionally, EGCG interacts with key residues in the oligomer's β-sheet core binding site, crucial for fibrillar aggregation. A better understanding of how anti-amyloid compounds remodelling AβOs could be valuable for the development of new therapeutic strategies targeting Aβ in AD. Further experimental validation using point mutations involving key residues could be useful to define whether the establishment of these interactions is crucial for the EGCG remodelling effect.

Adaptive constraints at the range edge of a widespread and expanding invasive plant.

Identifying the factors that facilitate and limit invasive species' range expansion has both practical and theoretical importance, especially at the range edges. Here, we used reciprocal common garden experiments spanning the North/South and East/West range that include the North American core, intermediate and range edges of the globally invasive plant, Johnsongrass (Sorghum halepense) to investigate the interplay of climate, biotic interactions (i.e. competition) and patterns of adaptation. Our results suggest that the rapid range expansion of Johnsongrass into diverse environments across wide geographies occurred largely without local adaptation, but that further range expansion may be restricted by a fitness trade-off that limits population growth at the range edge. Interestingly, plant competition strongly dampened Johnsongrass growth but did not change the rank order performance of populations within a garden, though this varied among gardens (climates). Our findings highlight the importance of including the range edge when studying the range dynamics of invasive species, especially as we try to understand how invasive species will respond to accelerating global changes.

The Deforestation and Biodiversity Risks of Power Plant Projects in Southeast Asia: A Big Data Spatial Analytical Framework

Ecosystem destruction and biodiversity loss are now widespread, extremely rapid, and among the top global anthropogenic risks both in terms of likelihood and overall impact. Thorough impact evaluation of these environmental abuses—essential for conservation and future project planning—requires good analysis of local ecological and environmental data in addition to social and economic impacts. We characterized the deforestation and biodiversity impacts of energy investments in Southeast Asia using multiple geospatial data sources related to forest cover and loss data from 2000 to 2018, other landcover data, and the location, type, and characteristics of energy investments. This study paid particular attention to different types of power plants and financing sources. We identified critical buffer zones and forest structures impacted by these projects in accordance with IUCN criteria and spatial ecology. The paper introduces a novel, replicable analytical framework that goes beyond earlier studies in which all forests are treated as equivalent. It characterizes forests based on spatial morphological structures such as core forest, edges, islands, and bridges, allowing for a more nuanced understanding of deforestation and its impacts on biodiversity. Preliminary findings suggest that projects financed by Chinese development banks pose different risks compared to non-Chinese financing. The study also reveals significant differences in biodiversity impacts based on the type of energy source, be it coal or hydro. The study offers critical insights into the trade-offs between energy development and biodiversity conservation. It provides actionable metrics and strategies for policymakers, conservationists, and development banks to prioritize forest and habitat preservation in Southeast Asia and globally.

Chemical bonding in americium oxides probed by X-ray spectroscopy

The electronic structure and the chemical state in Am binary oxides and Am-doped UO_2 were studied by means of X-ray absorption spectroscopy at shallow Am core (4d and 5d) edges. In particular, the Am 5f states were probed and the nature of their bonding to the oxygen states was analyzed. The interpretation of the experimental data was supported by the Anderson impurity model (AIM) calculations which took into account the full multiplet structure due to the interaction between 5f electrons as well as the interaction with the core hole. The sensitivity of the branching ratio of the Am 4d_{3/2} and 4d_{5/2} X-ray absorption lines to the chemical state of Am was shown using Am binary oxides as reference systems. The observed ratio for Am-doped UO_2 suggests that at least at low Am concentrations, americium is in the Am(III) state in the UO_2 lattice. To confirm the validity of the applied AIM approach, the analysis of the Am 4f X-ray photoelectron spectra of AmO_2 and Am_2O_3 was also performed which revealed a good agreement between experiment and calculations. As a whole, AmO_2 can be classified as the charge-transfer compound with the 5f occupancy (n_f) equal to 5.73 electrons, while Am_2O_3 is rather a Mott–Hubbard system with n_f = 6.05.

Arc Discharge System for Micromachining of Helical Fiber.

This article developed a micromachining system of arcing helical fiber with four electrodes to address the issues with conventional approaches to processing helical fibers, which have several uses. The technique may be utilized to create several types of helical fibers. First, the simulation demonstrates that the four-electrode arc's constant-temperature heating area is larger than the two-electrode arc's size. A large constant-temperature heating area is not only beneficial to the stress release of fiber, but also reduces the influence of fiber vibration and reduces the difficulty of device debugging. Then, a variety of helical fibers with various pitches were processed using the system presented in this research. By using a microscope, it can be observed that the cladding and core edges of the helical fiber are constantly smooth and the central core is tiny and off-axis, both of which are favorable for the propagation of optical waveguides. A low off-axis has been shown to minimize optical loss through modeling of energy coupling in spiral multi-core optical fibers. The transmission spectrum findings indicated that the device's insertion loss and transmission spectrum fluctuation were both minimal for four different types of multi-core spiral long-period fiber gratings with intermediate cores. These prove that the spiral fibers prepared by this system have excellent quality.

Analytical Hybrid Quasi-3D Transformer Leakage Inductance Model

Fast and accurate models of the transformer leakage inductance are crucial for optimization-based design of isolated converters. This article proposes an analytical hybrid Quasi-3D (HQ3D) model for E/U/ER/UR-core type transformers that combines the best suited submodels for each winding section of the transformer. A submodel is developed that directly calculates the leakage inductance contribution of curved winding sections based on the integration of the radially weighted magnetic energy density. The HQ3D model can describe arbitrary winding positions and its fully analytical nature guarantees optimal geometrical scalability. The only restriction is that the windings need to be parallel to the core edges. The HQ3D model is in the accuracy range of 3D finite-element method and verified with measurements on nine transformer prototypes with significantly different geometry ranges. The HQ3D model is rapidly executable, and therefore, well suited for optimizations of galvanically isolated converters.

New computational technique based on Bernstein polynomials and higher order finite difference schemes for temporal and spatial reactor calculations

In this research, two main objectives about solving the space time dependent diffusion equations are addressed. The first is to present higher order finite difference schemes at the core interior meshes and also at the points adjacent to the core boundaries, for spatial discretization. The second is to provide an accurate iteratively numerical method for temporal calculations based on convergent Bernstein polynomials. Bernstein series coefficients are estimated by using an easily computable recurrence relation. Estimation of the Bernstein recurrence relation requires nothing except the previous estimation of the series coefficients. Stability and convergence of the proposed method is discussed. It has proved that the method is conditionally stable and it has an exponential rate of convergence. Recently, we provided fourth and sixth order schemes to approximate the neutron leakage terms. These schemes provided highly accurate results but a large amount of these gained accuracies are lost due to using the classical second order finite difference formula at the core boundaries which uses three point stencils with order of accuracy OΔ2. Here, Lagrange’ polynomials are used to construct another second order scheme using five point stencils instead of three points at the core edges. It is proved that using central fourth order scheme at the interior meshes associated with the new scheme at the boundaries significantly improves the accuracy. Also, it is proved that using such finite difference schemes do not affect the system complexity and the system size remains unchanged. Homogenous and heterogeneous problems are considered to test the efficiency and the validity of the proposed method. In addition to the simplicity of the method algorithm, the results are so accurate that they can be compared with the recently methods that divided the same cores into thousands of fuel assemblies using mesh generator tools like GAMBIT or ICEM-CFD.

Electron energy loss spectroscopy database synthesis and automation of core-loss edge recognition by deep-learning neural networks

The ionization edges encoded in the electron energy loss spectroscopy (EELS) spectra enable advanced material analysis including composition analyses and elemental quantifications. The development of the parallel EELS instrument and fast, sensitive detectors have greatly improved the acquisition speed of EELS spectra. However, the traditional way of core-loss edge recognition is experience based and human labor dependent, which limits the processing speed. So far, the low signal–noise ratio and the low jump ratio of the core-loss edges on the raw EELS spectra have been challenging for the automation of edge recognition. In this work, a convolutional-bidirectional long short-term memory neural network (CNN-BiLSTM) is proposed to automate the detection and elemental identification of core-loss edges from raw spectra. An EELS spectral database is synthesized by using our forward model to assist in the training and validation of the neural network. To make the synthesized spectra resemble the real spectra, we collected a large library of experimentally acquired EELS core edges. In synthesize the training library, the edges are modeled by fitting the multi-Gaussian model to the real edges from experiments, and the noise and instrumental imperfectness are simulated and added. The well-trained CNN-BiLSTM network is tested against both the simulated spectra and real spectra collected from experiments. The high accuracy of the network, 94.9%, proves that, without complicated preprocessing of the raw spectra, the proposed CNN-BiLSTM network achieves the automation of core-loss edge recognition for EELS spectra with high accuracy.

CTNI-01. EFFECT OF PRE-OPERATIVE STEREOTACTIC RADIOSURGERY ON BRAIN METASTASIS: ANALYSIS OF DNA AND RNA GENOMIC PROFILES FROM PHASE-II CLINICAL TRIAL NCT03398694

Abstract BACKGROUND With improved systemic therapy that has limited impact on the intracranial compartment, the incidence of brain metastasis (BM) from solid cancers is rising. Treatment varies based on presentation, however, for patients with &amp;lt; 4 symptomatic BMs current clinical practice involves surgical resection followed by stereotactic radiosurgery (SRS) to the resection cavity. Post-operative SRS is associated with increased risk of leptomeningeal disease (LMD) and local recurrence. We hypothesize that pre-operative SRS will decrease the incidence of LMD as well as local recurrence, and increase patient’s OS by delivering a lethal dose of radiation to tumor cells before they are disturbed by surgical resection. In a Phase II clinical trial (NCT03398694) we are treating patients with 1-4 symptomatic BMs with pre-operative SRS while collecting DNA and RNA sequencing data from core and peripheral edges of the resected tumor to examine the genomic effects of SRS on tumor. METHODS Post-SRS resected tumor specimens were divided into two groups: ‘center’ and ‘periphery’ with respect to the center of SRS treatment with periphery within 50% isodose line. Previously resected untreated BMs were used as control. RESULTS/CONCLUSIONS Our initial data analyses show that pre-treatment with SRS results in significant genomic changes at DNA and RNA levels throughout the tumor when compared to non-radiated tumors. In matched center and peripheral samples DNA damage levels, measured by high impact mutation load, were similar indicating no evidence of radiation dose fall off effect on DNA damage. However, differing DNA damage locations and RNA expression profiles between center and matched samples are present, which may indicate unequal DNA repair processes between the center and periphery of tumors. Furthermore, global DNA mutation and RNA expression profiles differed significantly between the metastasis of different primary cancers signaling the importance of the primary cancer location in determining viable therapeutic targets.

What Controls Local Entrainment and Detrainment Rates in Simulated Shallow Convection?

Abstract The lack of consensus as to how entrainment and detrainment must be represented in convection parameterizations highlights the need for in-depth investigations of the processes driving lateral mixing in clouds. Direct estimates of entrainment and detrainment rates are here obtained from high-resolution simulations of shallow cumulus convection using a method that isolates the contributions from various competing processes. Moreover, cloud-averaged entrainment and detrainment rates are computed and correlated with bulk cloud and environmental properties. Detrainment is found to dominate in the middle of the cloud layer, and is locally driven by evaporation along cloud edges. Entrainment and detrainment events also occur due to changes in updraft velocity, but vertical acceleration and deceleration balance each other on average to yield no net entrainment/detrainment. In contrast, entrainment at cloud base is mostly related to condensation in dry updrafts surrounding the clouds, whereas detrainment at the top of the cloud layer is driven by buoyancy reversal. Cloud-averaged entrainment and detrainment rates both correlate preferentially with in-cloud vertical pressure gradients at all altitudes, but not with cloud-core buoyancy. No significant influence of environmental moisture on entrainment/detrainment was found, probably because of the very humid shell surrounding each cloud. Overall, these results suggest that entrainment and detrainment result from two concurring processes: a cloud-scale circulation driven by vertical pressure gradients, and small-scale, turbulent-like motions along the core edges generating mixing and, again, vertical pressure gradients.

Поэтика Homo Economicus

Homo Economicus details Vogl's major project of betraying secular theodicy in modern economics. We get to know a new kind of man, his origin and future. It seems, Vogl concludes, that we have found the latest realization of homo economicus – a person who goes hand in hand with a new way of subjectivation. The compulsion to identity has become a compulsion to non-identity, otherness, uniqueness. The production of these so-called patchwork identities has become an economic program; a program that generates items with a flexible core and fuzzy edges – items that can be in demand on the market. With this figure, the history of homo economicus comes to an end; it gives way to a new type of people: a type that acts economically precisely by producing itself, its own subjectivity and spirituality. Vogl Joseph. Poetics of Homo Economicus // Continent. Issue 4.1. 2014: 95-104.

Motif-h: a novel functional backbone extraction for directed networks

Dense networks are very pervasive in social analytics, biometrics, communication, architecture, etc. Analyzing and visualizing such large-scale networks are significant challenges, which are generally met by reducing the redundancy on the level of nodes or edges. Motifs, patterns of the higher order organization compared with nodes and edges, are recently found to be the novel fundamental unit structures of complex networks. In this work, we proposed a novel motif h-backbone (Motif-h) method to extract functional cores of directed networks based on both motif strength and h-bridge. Compared with the state-of-the-art method Motif-DF and Entropy, our method solves two main issues which are often found in existing methods: the Motif-h reconsiders weak ties into our candidate set, and those weak ties often have critical functions of bridges in networks; moreover, our method provides a trade-off between the motif size and the edge strength, which quantifies the core edges accordingly. In the simulations, we compare our method with Motif-DF in four real-world networks and found that Motif-h can streamline the extraction of crucial structures compared with the others with limited edges.

OMRT-9. Effect of Pre-Operative Stereotactic Radiosurgery on Brain Metastasis: Analysis of DNA and RNA Genomic Profiles from Phase-II Clinical Trial NCT03398694

BackgroundWith improved systemic therapy that has limited impact on the intracranial compartment, the incidence of brain metastasis (BM) from solid cancers is rising and negatively impacting patient’s overall survival (OS). Treatment varies based on presentation, however, for patients with <4 symptomatic BMs current clinical practice involves surgical resection followed by stereotactic radiosurgery (SRS) to the resection cavity. Post-operative SRS is associated with increased risk of leptomeningeal disease (LMD) and local recurrence in the follow-up period. We hypothesize that pre-operative SRS will decrease the incidence of LMD as well as local recurrence and increase patient’s OS by delivering a lethal dose of radiation to tumor cells before they are disturbed by surgical resection. In a Phase II clinical trial (NCT03398694) we are treating patients with 1–4 symptomatic BMs with pre-operative SRS while collecting DNA and RNA sequencing data from core and peripheral edges of the resected tumor to examine the genomic effects of SRS on tumor.MethodsPost-SRS resected tumor specimens were divided into two groups: ‘center’ and ‘periphery’ with respect to the center of SRS treatment with periphery within 50% isodose line. Previously resected untreated BMs were used as control. DNA and RNA were isolated from all samples for sequencing.ConclusionsOur initial analyses show that pre-treatment with SRS, results in significant genomic changes at DNA and RNA levels throughout the tumor, in both center as well as periphery. Furthermore, significant transcriptomic differences were noted among matched samples between the central and peripheral SRS locations implicating differential effect of SRS dosing within a tumor. Initial gene ontological analysis on non-small cell lung cancer samples demonstrated an overexpression of WNT and BMP signaling pathways (p <.001, p<.01). These pathways are typically involved in neuronal development, hinting that adaptation to the brain microenvironment was occurring post SRS treatment.

Oscillatory multipulsons: Dissipative soliton trains in bistable reaction-diffusion systems with cross diffusion of attractive-repulsive type.

One-dimensional localized sequences of bound (coupled) traveling pulses, wave trains with a finite number of pulses, are described in a piecewise-linear reaction-diffusion system of the FitzHugh-Nagumo type with linear cross-diffusion terms of opposite signs. The simplest case of two bound pulses, the paired-pulse waves (pulse pairs), is solved analytically. The solutions contain oscillatory tails in the wave profiles so that the pulse pairs consist of a double-peak core and wavy edges. Several pulse pairs with different profile shapes and propagation speeds can appear for the same parameter values of the model when the cross diffusion is dominant. The more general case of many bound pulses, multipulse waves, is studied numerically. It is shown that, dependent on the values of the cross-diffusion coefficients, the multipulse waves upon collision can pass through one another with unchanged size and shape, exhibiting soliton behavior. Moreover, multipulse collisions with the system boundaries can generate a rich variety of wave transformations: the transition from the multipulse waves to pulse-front waves and further to simple fronts or to annihilation as well the transition to solitary pulses or to multipulse waves with lower numbers of pulses. Analytical and numerical results for the pulse pairs agree well with each other.

Electron Energy Loss Structures of Terminated Scandium and Hafnium MXene Monolayers from First-Principles Calculations

In this study, the energy loss near edge structure (ELNES) of two-dimensional transition metal carbides and nitrides, namely ‘MXenes’, has been calculated in the density functional theory using the full-potential linearized augmented plane wave method. Calculations of band structure demonstrate that, in agreement with other studies, although MAX phases (bulks) and pristine MXenes (monolayers) are all metallic, some functionalized MXenes are semiconductors. For the first time, we have calculated the ELNES spectra of carbon and nitrogen K-edges in MAX M2AlX phases, pristine M2X MXenes and functionalized MXenes M2XT2 (M = Sc, Hf; X = C, N; T = F, OH, O) at magic angle conditions. Compared to the M2AlX bulks, the differences of energy between the main peaks in carbon and nitrogen K edges of M2X monolayers increase due to the slightly smaller bond lengths that can be used as a fingerprint for monolayers. The structures in ELNES spectra are sensitive to the chemical nature and the location of the T groups on the MXene surface. The energy position of the first main peak and the separation between the main peaks in the ELNES spectra of M2XT2 increase from T = O to T = OH and to T = F surface groups, respectivelyshows that the M-X bond length decreases from T = O to T = OH and to T = F. This short bond in M2XF2 leads to enhanced M-X interaction. The main features in carbon and nitrogen K edges correspond to the peaks in the unoccupied densities of states (DOS), namely σ* and π* states; however, the contribution of σ* states is dominant. Furthermore, for accurate insight into the excitonic effects (electron–hole coupling) in core edges, we have used the solving of the equation of motion of the two-particle Green’s function, the Bethe–Salpeter equation (BSE) to determine these effects on the K x-ray absorption near edge structure (XANES) of semiconductors Sc2CF2, Sc2C(OH)2, Sc2CO2 and Hf2CO2 MXenes. Calculations have been performed in two different levels of theory, with and without considering the excitonic effects. The excitonic effects considerably result in shifting the spectral features to the lower energies and changing in the intensity and overall shapes of the absorption spectra.

Reduced structural connectivity in cortico-striatal-thalamic network in neonates with congenital heart disease

Impaired brain development has been observed in newborns with congenital heart disease (CHD). We performed graph theoretical analyses and network-based statistics (NBS) to assess global brain network topology and identify subnetworks of altered connectivity in infants with CHD prior to cardiac surgery. Fifty-eight infants with critical/serious CHD prior to surgery and 116 matched healthy controls as part of the developing Human Connectome Project (dHCP) underwent MRI on a 3T system and high angular resolution diffusion MRI (HARDI) was obtained. Multi-tissue constrained spherical deconvolution, anatomically constrained probabilistic tractography (ACT) and spherical-deconvolution informed filtering of tractograms (SIFT2) was used to construct weighted structural networks. Network topology was assessed and NBS was used to identify structural connectivity differences between CHD and control groups. Structural networks were partitioned into core and peripheral nodes, and edges classed as core, peripheral, or feeder. NBS identified one subnetwork with reduced structural connectivity in CHD infants involving basal ganglia, amygdala, hippocampus, cerebellum, vermis, and temporal and parieto-occipital lobe, primarily affecting core nodes and edges. However, we did not find significantly different global network characteristics in CHD neonates. This locally affected sub-network with reduced connectivity could explain, at least in part, the neurodevelopmental impairments associated with CHD.

Construction of a knowledge graph for diabetes complications from expert-reviewed clinical evidences

A knowledge graph is a structured representation of data that can express entity and relational knowledge. More attention has been paid to the study of a clinical knowledge graph, especially in the field of chronic diseases. However, knowledge graph construction is based mainly on electronic medical records and other data sources, and the authority of the constructed knowledge graph presents some problems. Therefore, regarding the quality of evidence, this study, in combination with experimental research on system evaluation and meta-analysis presents some new information, On the basis of evidence-based medicine (EBM), the secondary results of systematic evaluation and meta-analyses of social, psychological, and behavioral aspects were extracted as data for the core nodes and edges of a knowledge graph to construct a graph of type 2 diabetes (T2D) and its complications. In this study, relevant life-style evidence that are factors for the risk of diabetic retinopathy (DR), diabetic nephropathy (DN), diabetic foot (DF), and diabetic depression (DD), and the results of several of the relevant clinical test, including bariatric surgery, myopia, lipid-lowering drugs, lipid-lowering drug duration, blood glucose control, disease course, glycosylated hemoglobin, fasting blood glucose, hypertension, sex, smoking and other common lifestyle characteristics were finally extracted. The evidence-based knowledge graph of the DM complications was constructed by extracting relevant disease, risk factors, risk outcomes, and other diabetes entities and the strength of the data for the odds ratio (OR) or relative risk (RR) correlations from clinical evidence. Moreover, the risk prediction models constructed using a logistic model were incorporated into the knowledge graph to visualize the risk score of DM complications for each user. In short, the EBM-powered construction of the knowledge graph could provide high-quality information to support decisions for the prevention and control of diabetes and its complications.